MIT engineers develop a way to determine how the surfaces of materials behave
Using machine learning, the computational method can provide details of how materials work as catalysts, semiconductors, or battery components.
Using machine learning, the computational method can provide details of how materials work as catalysts, semiconductors, or battery components.
MIT Digital Learning Lab and Empowr pilot a new internship program.
The Nano Summit highlights nanoscale research across multiple disciplines at MIT.
The work demonstrates control over key properties leading to better performance.
Fall 2023 Wulff Lecture speaker Sossina Haile ’86, PhD ’92 uses ammonia and a “superprotonic” material for efficient and eco-friendly energy generation.
Passionate about materials science “from the atom to the system,” Elsa Olivetti brings a holistic approach to sustainability to her teaching, research, and coalition-building.
The LIRAS technique could speed up the development of acoustic lenses, impact-resistant films, and other futuristic materials.
MIT DMSE hosts its first-ever jobs fair, attracting industry giants, startups, and students for networking and career exploration.
Thirteen new graduate student fellows will pursue exciting new paths of knowledge and discovery.
James Fujimoto, Eric Swanson, and David Huang are recognized for their technique to rapidly detect diseases of the eye; Subra Suresh is honored for his commitment to research and collaboration across borders.
The Spark Photonics Foundation works with educators to get K-12 and college students interested in STEM fields, including advanced manufacturing and semiconductors.
The fibers could help with testing treatments for nerve-related pain.
Five MIT faculty, along with seven additional affiliates, are honored for outstanding contributions to medical research.
Martin Luther King Jr. Visiting Professors and Scholars will enhance and enrich the MIT community through engagement with students and faculty.
Co-directors Youssef Marzouk and Nicolas Hadjiconstantinou describe how the standalone degree aims to train students in cross-cutting aspects of computational science and engineering.